• DOI:10.1007/978-3-319-99957-9_6
• Corpus ID: 52095136

Machine Learning for Inductive Theorem Proving

@inproceedings{Jiang2018MachineLF,
  title={Machine Learning for Inductive Theorem Proving},
  author={Yaqing Jiang and P. Papapanagiotou and Jacques D. Fleuriot},
  booktitle={AISC},
  year={2018}
}

• Yaqing Jiang, P. Papapanagiotou, Jacques D. Fleuriot
• Published in AISC 16 September 2018
• Computer Science

Over the past few years, machine learning has been successfully combined with automated theorem provers to prove conjectures from proof assistants. However, such approaches do not usually focus on inductive proofs. In this work, we explore a combination of machine learning, a simple Boyer-Moore model and ATPs as a means of improving the automation of inductive proofs in the proof assistant HOL Light. We evaluate the framework using a number of inductive proof corpora. In each case, our approach achieves a higher success rate than running ATPs or the Boyer-Moore tool individually.